# What are best practices for choosing the beta for an F-measure score?

There's some discussion on what F-measure means. I understand that the beta parameter determines the weight of recall in the combined score. In specific one answer states that "for good models using the $$F_{\beta}$$ implies you consider false negatives $$\beta^2$$ times more costly than false positives." beta < 1 lends more weight to precision, while beta > 1 favors recall (beta -> 0 considers only precision, beta -> +inf only recall).

If you want to weight precision or recall higher than the other, how do you decide on the beta? I'm a bit unclear on the math behind the F-measure, so does a beta = .5 mean that precision is weighted 2x as much as recall?

• From $\beta^2$, $\beta=0.5$ would suggest that precision would be weighted 4 times as much as recall, at least according to the one answer cited.
– Carl
Feb 29, 2020 at 2:14